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RidgeBinaryLogisticFit <- function(y, xd, freq, tolerance = 1e-05, maxiter = 100, penalization = 0.2) {
# Fits a Binary Logistic Regression
n <- dim(xd)[1]
m <- dim(xd)[2]
beta = matrix(0, m, 1)
#freq=freq/sum(freq)
err = 0.1
iter = 0
while ((err > tolerance) & (iter < maxiter)) {
iter = iter + 1
betaold = beta
eta = xd %*% beta
mu = exp(eta)/(1 + exp(eta))
v = (mu * (1 - mu)) * freq
vv = diag(1, n, n)
diag(vv) <- v
Imod=diag(m)
Imod[1,1]=0
In = (t(xd) %*% vv %*% xd) + 2 * penalization * Imod
U = t(xd) %*% ((y - mu) * freq) - 2 * penalization * Imod %*% betaold
beta = betaold + ginv(In) %*% U
err = sum(abs(betaold - beta))
}
return(beta)
}
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